Key Responsibilities

- Build and ship end-to-end AI/ML solutions for enterprise-scale Generative AI products, including LLM-based applications, RAG pipelines, and agentic workflows.
- Hands-on development of Conversational AI agents, RAG systems, and NLP pipelines.
- Implement, fine-tune, and deploy large language models (LLMs) such as GPT-4, Claude, Llama, and Mistral, and benchmark them for accuracy, latency, and cost.
- Build data pipelines and work with vector databases (Pinecone, Weaviate, pgvector, Qdrant) to support retrieval-augmented generation (RAG) and embedding-based search.
- Contribute to responsible AI practices, model evaluation, and observability/monitoring for the systems you own.
- Work closely with engineering, product, and data science teams to translate business requirements into working AI features.
- Integrate third-party AI APIs, MLOps tooling, and cloud AI services (AWS SageMaker, Azure OpenAI, GCP Vertex AI - Any one).
- Guide and review the work of junior AI engineers; set coding, testing, and deployment standards within the team.
- Track developments in foundation models, multi-modal AI, and emerging GenAI techniques, and apply them to product improvements.
- Take proof-of-concept (PoC) projects through to production-grade deployments with a focus on latency, cost, and reliability.




- Ensure AI systems comply with data privacy regulations (GDPR, DPDP) and internal security policies.

Required Qualifications

- 5+ years of overall software/ML engineering experience, with at least 2 years hands-on in Generative AI and Machine Learning in a production setting.
- Strong proficiency in Python and ML frameworks: PyTorch, TensorFlow, Hugging Face Transformers.
- Proven experience building and deploying LLM-based applications (prompt engineering, fine-tuning, RLHF).
- Strong working knowledge of RAG architectures, embedding models, and vector databases.
- Experience with cloud platforms (AWS, Azure, or GCP) and containerisation (Docker, Kubernetes).
- Solid understanding of MLOps practices: CI/CD for ML, model versioning, monitoring, and drift detection.
- Hands-on experience with orchestration frameworks such as LangChain, LlamaIndex, or AutoGen.
- Good system design skills with the ability to build scalable, fault-tolerant AI services.
- Strong communication skills, with the ability to explain technical trade-offs to non-technical stakeholders.
- Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field (or equivalent practical experience).

📌 AI / ML Architect (Contractual) (India)
🏢 Wishtree Technologies
📍 India